Recommending friends and locations based on individual location history

  • Yu Zheng ,
  • Lizhu Zhang ,
  • Zhengxin Ma ,
  • ,
  • Wei-Ying Ma

ACM Transactions on the Web |

The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply to some extent users’ interests in places, and bring us opportunities to understand the correlation between users and locations. In this article, we move towards this direction, and report on a personalized friend & location recommender for the geographical information systems (GIS) on the Web. First, in this recommender system a particular individual’s visits to a geospatial region in the real world are used as their implicit ratings on that region. Second, we measure the similarity between users in terms of their location histories, and recommend each user a group of potential friends in a GIS community. Third, we estimate an individual’s interests in a set of unvisited regions by involving his/her location history and those of other users. Some unvisited locations that might match their tastes can be recommended to the individual. A framework, referred to as a hierarchical-graph-based similarity measurement (HGSM), is proposed to uniformly model each individual’s location history, and effectively measure the similarity among users. In this framework, we take into account three factors: 1) the sequence property of people’s outdoor movements, 2) the visited popularity of a geospatial region and 3) the hierarchical property of geographic spaces. Further, we incorporated a content-based method into a user-based collaborative filtering algorithm, which uses HGSM as the user similarity measure, to estimate the rating of a user on an item. We evaluated this recommender system based on the GPS data collected by 75 subjects over a period of 1 year in the real world. As a result, HGSM outperforms related similarity measures, comprising of similarity-by-count, the cosine similarity and Pearson similarity measures. Moreover, beyond the item-based CF method and random recommendations, our system provides users with more attractive locations and better user experiences of recommendation.

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GeoLife GPS Trajectories

August 9, 2012

This is a GPS trajectory dataset collected in (Microsoft Research Asia) GeoLife project by 182 users in a period of over two years (from April 2007 to August 2012). This trajectory dataset can be used in many research fields, such as mobility pattern mining, user activity recognition, location-based social networks, location privacy, and location recommendation. The following heat maps visualize its distribution in Beijing. Please cite the following two papers when using this dataset. [1] Yu Zheng, Quannan Li, Yukun Chen, Xing Xie. Understanding Mobility Based on GPS Data. In Proceedings of ACM conference on Ubiquitous Computing (UbiComp 2008), Seoul, Korea. ACM Press: 312-321. [2] Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of International conference on World Wild Web (WWW 2009), Madrid Spain. ACM Press: 791-800.